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Rice ( Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

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  • Li-Wei Liu

    (Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung County 91201, Taiwan
    Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77840, USA)

  • Chun-Tang Lu

    (Crop Science Division, Taiwan Agricultural Research Institute, Council of Agriculture, Executive Yuan, Taichung City 413008, Taiwan)

  • Yu-Min Wang

    (General Research Service Center, National Pingtung University of Science and Technology, Pingtung County 91201, Taiwan)

  • Kuan-Hui Lin

    (Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung County 91201, Taiwan)

  • Xingmao Ma

    (Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77840, USA)

  • Wen-Shin Lin

    (Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung County 91201, Taiwan)

Abstract

Rice ( Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

Suggested Citation

  • Li-Wei Liu & Chun-Tang Lu & Yu-Min Wang & Kuan-Hui Lin & Xingmao Ma & Wen-Shin Lin, 2022. "Rice ( Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms," Agriculture, MDPI, vol. 12(1), pages 1-11, January.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:1:p:59-:d:716768
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    References listed on IDEAS

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    1. Orlando, Francesca & Alali, Sumer & Vaglia, Valentina & Pagliarino, Elena & Bacenetti, Jacopo & Bocchi, Stefano & Bocchi, Stefano, 2020. "Participatory approach for developing knowledge on organic rice farming: Management strategies and productive performance," Agricultural Systems, Elsevier, vol. 178(C).
    2. Assefa, Yared & Yadav, Sudhir & Mondal, Manoranjan K. & Bhattacharya, Jayanta & Parvin, Rokhsana & Sarker, Shilpi R. & Rahman, Mahabubur & Sutradhar, Asish & Prasad, P.V. Vara & Bhandari, Humnath & Sh, 2021. "Crop diversification in rice-based systems in the polders of Bangladesh: Yield stability, profitability, and associated risk," Agricultural Systems, Elsevier, vol. 187(C).
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    2. Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2024. "Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling," Agricultural Water Management, Elsevier, vol. 294(C).
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